title: Eigenspectra, a robust regression method for multiplexed Raman spectra analysis.
abstract: With the latest development of Surface Enhanced Raman Scattering (SERS) nanoparticles, Raman spectroscopy now can be extended to bioimaging and biosensing. In this study, we demonstrate the ability of Raman spectroscopy to separate multiple spectral fingerprints using Raman nanotags. A machine learning method is proposed to estimate the mixing ratios of sources from mixture signals. It decomposes the mixture signals into components for both best representation and most relating to mixing ratios. Then regression coefficients are calculated for the prediction. The robustness of the method was compared with least squares and weighted least squares methods.
authors.affiliation: Computer Science and Engineering Department, University of Texas at Arlington, Arlington, Texas 76013, USA. shuo.li@uta.edu
authors.forename: Shuo
authors.initials: S
authors.lastname: Li
authors.forename: James O
authors.initials: JO
authors.lastname: Nyagilo
authors.forename: Digant P
authors.initials: DP
authors.lastname: Dave
authors.forename: Baoju
authors.initials: B
authors.lastname: Zhang
authors.forename: Jean
authors.initials: J
authors.lastname: Gao
mesh_terms: D001185:Artificial Intelligence; D048588:Benzoxazines / Q000737:chemistry; D002232:Carbocyanines / Q000737:chemistry; D004396:Coloring Agents; D016018:Least-Squares Analysis; D053758:Nanoparticles; D012044:Regression Analysis; D013059:Spectrum Analysis, Raman / Q000379:methods*; D013499:Surface Properties
chemical_list: C529653:3,3'-diethyl-2,2'-thiatricarbocyanine; D048588:Benzoxazines; D002232:Carbocyanines; D004396:Coloring Agents; C028911:cresyl violet